MINT, the molecular interaction database: 2009 update

By Arnaud Céol, A. Chatr-aryamontri, L. Licata, Daniele Peluso, Leonardo Briganti, L. Perfetto, L. Castagnoli, G. Cesareni. Nucleic Acids Res., 2009

550 citations recorded by the source corpus.

MINT (http://mint.bio.uniroma2.it/mint) is a public repository for molecular interactions reported in peer-reviewed journals. Since its last report, MINT has grown considerably in size and evolved in scope to meet the requirements of its users. The main changes include a more precise definition of the curation policy and the development of an enhanced and user-friendly interface to facilitate the analysis of the ever-growing interaction dataset. MINT has adopted the PSI-MI standards for the annotation and for the representation of molecular interactions and is a member of the IMEx consortium.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4575b722fb97c303d93671198431fc349a2469a7

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Citations in the corpus

Phospho.ELM: a database of phosphorylation sites—update 2011 (2010, background): “Inaddition, we encourage our users to explore the links to other resources that integrate information on signaling networks or protein–protein interactions, such as MINT (30) and STRING (31), to expand their knowledge of the phosphoprotein substrates.”
The IntAct molecular interaction database in 2012 (2011, background): “There are many systems available for scoring protein interaction data, based on various criteria including interaction evidences (15).”
Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping (2011, methodology): “While there are already several interaction databases implementing the PSICQUIC interface [43-50], Bio::Homology:: InterologWalk currently relies on EBI IntAct [36] as its source of experimental interactions.”
ConsensusPathDB: toward a more complete picture of cell biology (2010, unknown): “Apart from extending the human functional interaction network, we have created ConsensusPathDB instances for two more organisms: Saccharomyces cerevisiae and Mus musculus, integrating eight interaction resources each: Reactome (11), KEGG (12), BioCyc (13), IntAct (5), DIP (14), MINT (15), BioGRID (1”
PINA v2.0: mining interactome modules (2011, background): “The first version of PINA (11) has established a non-redundant PPI database, updated quarterly based on the integration of protein interaction data from six publicly available, manually curated databases: IntAct (12), MINT (13), BioGRID (14), DIP (15), HPRD (16) and MIPS MPact (17).”
MINT, the molecular interaction database: 2012 update (2011, methodology): “MINT was one of the first PPI database to associate to each interaction a score estimating the reliability of the interaction, given the available experimental evidence (3,12).”
Network-based in silico drug efficacy screening (2016, background): “Wetookthehuman protein–protein interaction (PPI) network compiled by Menche et al. 12 that contained experimentally documented human physical interactions from TRANSFAC 38 , IntAct 39 , MINT 40 , BioGRID 41 , HPRD 42 , KEGG 43 , BIGG 44 , CORUM 45 , PhosphoSitePlus 46 and a large scale signalling ne”
Interactome3D: adding structural details to protein networks (2013, methodology): “This latter criterion is applied only to entries that are curated according to the IMEx 53 or MIMIx 54 standards—that is, these entries have an IMEx identifier or a flag is present that specifies that the curation depth is either IMEx, MIMIx or light curation 55 .”
Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks (2010, background): “Some of the primary databases are DIP [19], IntAct [20], and MINT [21], which are the core founders of IMEx, the international consortium of molecular interaction (MI) database providers.”
The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function (2010, background): “Data sets are collected from publicly available databases, including co-expression data from Gene Expression Omnibus (GEO) (1); physical and genetic interaction data from BioGRID (2); predicted protein interaction data based on orthology from I2D (3); and pathway and molecular interaction data from ”